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SUMMARY:Franca Hoffmann (University of Bonn)
DTSTART:20210427T120000Z
DTEND:20210427T130000Z
DTSTAMP:20260423T034447Z
UID:DSCSS/19
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/DSCSS/19/">K
 alman-Wasserstein Gradient Flows</a>\nby Franca Hoffmann (University of Bo
 nn) as part of Data Science and Computational Statistics Seminar\n\n\nAbst
 ract\nWe study a class of interacting particle systems that may be used fo
 r optimization. By considering the mean-field limit one obtains a nonlinea
 r Fokker-Planck equation. This equation exhibits a gradient structure in p
 robability space\, based on a modified Wasserstein distance which reflects
  particle correlations: the Kalman-Wasserstein metric. This setting gives 
 rise to a methodology for calibrating and quantifying uncertainty for para
 meters appearing in complex computer models which are expensive to run\, a
 nd cannot readily be differentiated. This is achieved by connecting the in
 teracting particle system to ensemble Kalman methods for inverse problems.
  This is joint work with Alfredo Garbuno-Inigo (Caltech)\, Wuchen Li (UCLA
 ) and Andrew Stuart (Caltech).\n
LOCATION:https://researchseminars.org/talk/DSCSS/19/
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